TPG's involvement in Fractal includes a mix of primary investment and a secondary share purchase from funds advised by buyout firm and existing investor Apax Partners. The share purchase deal will close by the first quarter of this year, following which Apax will continue to remain a major shareholder of the startup. Co-founded by Srikanth Velamakanni and Pranay Agrawal in Mumbai in 2000, New York-headquartered Fractal provides artificial intelligence and analytics solutions to Fortune 500 companies. "We continue to see great momentum in how clients are leveraging AI to accelerate digital transformation," Velamakanni said in a statement. The company's products include Qure.ai that helps radiologists make diagnostic decisions, and Theremin.ai,

Men who only briefly catch a glimpse of a woman are much more likely to overestimate how attractive she is than a woman glimpsing a man, a study reveals. Researchers, led by Murdoch University, in Perth Australia, worked with nearly 400 volunteers, asking them to rate the attractiveness of people of the opposite-sex from a blurry image, and then from a clear image. The results showed that on average men overestimate women's attractiveness, whereas on average women underestimate men's attractiveness. If you've been having trouble finding love on dating apps, you might want to try dating one of your friends. A study looked at data from just under 2,000 couples of different demographics in Canada.

Jan 5 (Reuters) - Artificial intelligence technology startup Fractal said on Wednesday it has received a $360 million investment from private equity firm TPG through its Asia focused investment platform. TPG's involvement in Fractal includes a mix of primary investment and a secondary share purchase from funds advised by buyout firm and existing investor Apax Partners. The share purchase deal will close by the first quarter of this year, following which Apax will continue to remain a major shareholder of the startup. Co-founded by Srikanth Velamakanni and Pranay Agrawal in Mumbai in 2000, New York-headquartered Fractal provides artificial intelligence and analytics solutions to Fortune 500 companies. "We continue to see great momentum in how clients are leveraging AI to accelerate digital transformation," Velamakanni said in a statement.

We perform a systematic analysis of the quality of fit of the stochastic block model (SBM) for 275 empirical networks spanning a wide range of domains and orders of size magnitude. We employ posterior predictive model checking as a criterion to assess the quality of fit, which involves comparing networks generated by the inferred model with the empirical network, according to a set of network descriptors. We observe that the SBM is capable of providing an accurate description for the majority of networks considered, but falls short of saturating all modeling requirements. In particular, networks possessing a large diameter and slow-mixing random walks tend to be badly described by the SBM. However, contrary to what is often assumed, networks with a high abundance of triangles can be well described by the SBM in many cases. We demonstrate that simple network descriptors can be used to evaluate whether or not the SBM can provide a sufficiently accurate representation, potentially pointing to possible model extensions that can systematically improve the expressiveness of this class of models.

Frame shift is a cross-linguistic phenomenon in translation which results in corresponding pairs of linguistic material evoking different frames. The ability to predict frame shifts enables automatic creation of multilingual FrameNets through annotation projection. Here, we propose the Frame Shift Prediction task and demonstrate that graph attention networks, combined with auxiliary training, can learn cross-linguistic frame-to-frame correspondence and predict frame shifts.

Capacitive sensing is a prominent technology that is cost-effective and low power consuming with fast recognition speed compared to existing sensing systems. On account of these advantages, Capacitive sensing has been widely studied and commercialized in the domains of touch sensing, localization, existence detection, and contact sensing interface application such as human-computer interaction. However, as a non-contact proximity sensing scheme is easily affected by the disturbance of peripheral objects or surroundings, it requires considerable sensitive data processing than contact sensing, limiting the use of its further utilization. In this paper, we propose a real-time interface control framework based on non-contact hand motion gesture recognition through processing the raw signals, detecting the electric field disturbance triggered by the hand gesture movements near the capacitive sensor using adaptive threshold, and extracting the significant signal frame, covering the authentic signal intervals with 98.8% detection rate and 98.4% frame correction rate. Through the GRU model trained with the extracted signal frame, we classify the 10 hand motion gesture types with 98.79% accuracy. The framework transmits the classification result and maneuvers the interface of the foreground process depending on the input. This study suggests the feasibility of intuitive interface technology, which accommodates the flexible interaction between human to machine similar to Natural User Interface, and uplifts the possibility of commercialization based on measuring the electric field disturbance through non-contact proximity sensing which is state-of-the-art sensing technology.

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

To be future-ready, companies must start combining AI, human skills, and trusted partnerships now. After all, climate change is happening now. Rising sea levels, and intensifying wildfires, storms, droughts and floods hammer home that message every day. The damage is undeniable, and the clock is ticking. Let's be clear: clean energy and efficient energy management are key to attacking the climate crisis.

In public proceedings, the Legal Board of Appeal of the EPO confirmed that under the European Patent Convention (EPC), an inventor designated in a patent application must be a human being. This was the judgement in combined cases J 8/20 and J 9/20, where the board just dismissed the applicant's appeal. Here, both the applications were made by a Missouri physicist Stephen Thaler, whose AI-system DABUS had made the inventions. Device for the Autonomous Bootstrapping of Unified Sentience, or DABUS, is a computer system programmed to invent by itself. It is, basically, a swarm of disconnected neutral nets that can continuously generate thought processes and even memories that can, over time, generate new and inventive outputs independently.

Beijing is calling on the world's nuclear powers to expand discussions on global security to include emerging threats, following a rare multilateral pledge to temper the risks of nuclear war. Fu Cong, director-general of the Chinese Foreign Ministry's Arms Control Department, told reporters in Beijing on Tuesday that the so-called P5 nations -- China, France, Russia, the U.S. and U.K. -- should talk "more directly" about global security. "Strategic stability goes beyond nuclear," he said. "Our idea is to expand the subject of the P5 process so we could discuss not only the nuclear issues, but also other issues related to strategic stability, including outer space, missile defense, even AI and other emerging technologies." The briefing took place after the five nations -- all permanent members of the United Nations Security Council -- issued a joint statement Monday pledging to dial back the risk of a nuclear conflict.